Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations4845
Missing cells206
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory492.1 KiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric12

Alerts

tavg is highly overall correlated with tmax and 2 other fieldsHigh correlation
tmax is highly overall correlated with tavg and 2 other fieldsHigh correlation
tmin is highly overall correlated with tavg and 1 other fieldsHigh correlation
tsun is highly overall correlated with tavg and 1 other fieldsHigh correlation
wpgt is highly overall correlated with wspdHigh correlation
wspd is highly overall correlated with wpgtHigh correlation
wdir has 192 (4.0%) missing valuesMissing
date has unique valuesUnique
prcp has 2497 (51.5%) zerosZeros
snow has 4500 (92.9%) zerosZeros
tsun has 766 (15.8%) zerosZeros

Reproduction

Analysis started2024-09-14 08:19:25.370594
Analysis finished2024-09-14 08:19:51.115640
Duration25.75 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

UNIQUE 

Distinct4845
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size75.7 KiB
Minimum2011-06-01 00:00:00
Maximum2024-09-05 00:00:00
2024-09-14T10:19:51.327426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:51.497910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tavg
Real number (ℝ)

HIGH CORRELATION 

Distinct368
Distinct (%)7.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10.959529
Minimum-12.6
Maximum29.5
Zeros10
Zeros (%)0.2%
Negative348
Negative (%)7.2%
Memory size75.7 KiB
2024-09-14T10:19:52.110819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-12.6
5-th percentile-1
Q14.9
median10.9
Q317.1
95-th percentile22.9
Maximum29.5
Range42.1
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation7.6437958
Coefficient of variation (CV)0.69745658
Kurtosis-0.76176831
Mean10.959529
Median Absolute Deviation (MAD)6.1
Skewness-0.068048829
Sum53077
Variance58.427614
MonotonicityNot monotonic
2024-09-14T10:19:52.453899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 30
 
0.6%
10.5 30
 
0.6%
4.8 30
 
0.6%
18.6 30
 
0.6%
13.7 30
 
0.6%
10.4 29
 
0.6%
2.9 28
 
0.6%
16.1 28
 
0.6%
6.8 27
 
0.6%
6.7 27
 
0.6%
Other values (358) 4554
94.0%
ValueCountFrequency (%)
-12.6 1
 
< 0.1%
-12 2
< 0.1%
-11.6 3
0.1%
-10.9 1
 
< 0.1%
-10.4 1
 
< 0.1%
-10.2 1
 
< 0.1%
-10 1
 
< 0.1%
-9.9 1
 
< 0.1%
-9.8 1
 
< 0.1%
-9.5 2
< 0.1%
ValueCountFrequency (%)
29.5 1
< 0.1%
28.6 1
< 0.1%
28.4 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
27.8 2
< 0.1%
27.6 2
< 0.1%
27.5 2
< 0.1%
27.4 2
< 0.1%
27.3 1
< 0.1%

tmin
Real number (ℝ)

HIGH CORRELATION 

Distinct331
Distinct (%)6.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.7052447
Minimum-16.6
Maximum21.5
Zeros18
Zeros (%)0.4%
Negative831
Negative (%)17.2%
Memory size75.7 KiB
2024-09-14T10:19:52.662260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-16.6
5-th percentile-3.5
Q11.4
median6.7
Q312.2
95-th percentile16.7
Maximum21.5
Range38.1
Interquartile range (IQR)10.8

Descriptive statistics

Standard deviation6.6338468
Coefficient of variation (CV)0.98935194
Kurtosis-0.65612343
Mean6.7052447
Median Absolute Deviation (MAD)5.4
Skewness-0.14255731
Sum32473.5
Variance44.007924
MonotonicityNot monotonic
2024-09-14T10:19:52.863967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.1 36
 
0.7%
0.7 35
 
0.7%
3.3 34
 
0.7%
12.9 33
 
0.7%
11 32
 
0.7%
2.8 32
 
0.7%
7 32
 
0.7%
0.3 32
 
0.7%
3.6 31
 
0.6%
0.2 31
 
0.6%
Other values (321) 4515
93.2%
ValueCountFrequency (%)
-16.6 1
< 0.1%
-16.1 1
< 0.1%
-15 1
< 0.1%
-14.8 1
< 0.1%
-14.5 1
< 0.1%
-14.4 1
< 0.1%
-13.9 1
< 0.1%
-13.7 1
< 0.1%
-13.4 1
< 0.1%
-13.2 1
< 0.1%
ValueCountFrequency (%)
21.5 1
< 0.1%
21.4 1
< 0.1%
21.3 1
< 0.1%
21.1 1
< 0.1%
20.8 2
< 0.1%
20.7 1
< 0.1%
20.6 2
< 0.1%
20.5 1
< 0.1%
20.4 2
< 0.1%
20.3 2
< 0.1%

tmax
Real number (ℝ)

HIGH CORRELATION 

Distinct420
Distinct (%)8.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.640058
Minimum-9.7
Maximum36.9
Zeros8
Zeros (%)0.2%
Negative149
Negative (%)3.1%
Memory size75.7 KiB
2024-09-14T10:19:53.041524image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-9.7
5-th percentile1.2
Q18.6
median15.6
Q322.9
95-th percentile29.8
Maximum36.9
Range46.6
Interquartile range (IQR)14.3

Descriptive statistics

Standard deviation9.0344607
Coefficient of variation (CV)0.57764881
Kurtosis-0.81440384
Mean15.640058
Median Absolute Deviation (MAD)7.2
Skewness-0.052019169
Sum75744.8
Variance81.621481
MonotonicityNot monotonic
2024-09-14T10:19:53.227989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5 31
 
0.6%
19.4 29
 
0.6%
11.9 29
 
0.6%
18.9 28
 
0.6%
15.5 25
 
0.5%
19.3 25
 
0.5%
22.2 25
 
0.5%
4.9 25
 
0.5%
22.6 24
 
0.5%
9.7 24
 
0.5%
Other values (410) 4578
94.5%
ValueCountFrequency (%)
-9.7 1
< 0.1%
-9.4 1
< 0.1%
-9.1 1
< 0.1%
-8.4 2
< 0.1%
-8.3 1
< 0.1%
-8.1 1
< 0.1%
-7.6 1
< 0.1%
-7.5 1
< 0.1%
-7.2 1
< 0.1%
-7.1 1
< 0.1%
ValueCountFrequency (%)
36.9 2
< 0.1%
36.8 1
 
< 0.1%
36.5 1
 
< 0.1%
36.2 1
 
< 0.1%
35.9 2
< 0.1%
35.8 2
< 0.1%
35.6 2
< 0.1%
35.5 3
0.1%
35.4 1
 
< 0.1%
35.3 2
< 0.1%

prcp
Real number (ℝ)

ZEROS 

Distinct281
Distinct (%)5.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.6315053
Minimum0
Maximum91.5
Zeros2497
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:53.437846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile13.9
Maximum91.5
Range91.5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.9459392
Coefficient of variation (CV)2.2595201
Kurtosis32.654201
Mean2.6315053
Median Absolute Deviation (MAD)0
Skewness4.5415193
Sum12744.38
Variance35.354192
MonotonicityNot monotonic
2024-09-14T10:19:53.660139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2497
51.5%
0.1 144
 
3.0%
0.2 114
 
2.4%
0.3 84
 
1.7%
0.4 54
 
1.1%
0.5 53
 
1.1%
0.6 52
 
1.1%
0.7 52
 
1.1%
0.9 50
 
1.0%
1.3 48
 
1.0%
Other values (271) 1695
35.0%
ValueCountFrequency (%)
0 2497
51.5%
0.1 144
 
3.0%
0.2 114
 
2.4%
0.3 84
 
1.7%
0.4 54
 
1.1%
0.5 53
 
1.1%
0.6 52
 
1.1%
0.7 52
 
1.1%
0.8 43
 
0.9%
0.9 50
 
1.0%
ValueCountFrequency (%)
91.5 1
< 0.1%
71.2 1
< 0.1%
66.9 1
< 0.1%
61.7 1
< 0.1%
56.9 1
< 0.1%
56.7 1
< 0.1%
54.5 1
< 0.1%
49.2 1
< 0.1%
46.4 1
< 0.1%
46.3 1
< 0.1%

snow
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9453044
Minimum0
Maximum420
Zeros4500
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:53.858682image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum420
Range420
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.262493
Coefficient of variation (CV)4.9061677
Kurtosis77.249778
Mean4.9453044
Median Absolute Deviation (MAD)0
Skewness7.5208776
Sum23960
Variance588.66856
MonotonicityNot monotonic
2024-09-14T10:19:54.074497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 4500
92.9%
40 43
 
0.9%
10 42
 
0.9%
20 36
 
0.7%
30 36
 
0.7%
50 29
 
0.6%
70 22
 
0.5%
80 20
 
0.4%
60 17
 
0.4%
90 16
 
0.3%
Other values (20) 84
 
1.7%
ValueCountFrequency (%)
0 4500
92.9%
10 42
 
0.9%
20 36
 
0.7%
30 36
 
0.7%
40 43
 
0.9%
50 29
 
0.6%
60 17
 
0.4%
70 22
 
0.5%
80 20
 
0.4%
90 16
 
0.3%
ValueCountFrequency (%)
420 1
< 0.1%
410 1
< 0.1%
370 1
< 0.1%
310 1
< 0.1%
300 1
< 0.1%
250 2
< 0.1%
230 1
< 0.1%
220 1
< 0.1%
210 1
< 0.1%
200 2
< 0.1%

wdir
Real number (ℝ)

MISSING 

Distinct360
Distinct (%)7.7%
Missing192
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean197.09843
Minimum0
Maximum359
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:54.537578image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q1100
median233
Q3265
95-th percentile321
Maximum359
Range359
Interquartile range (IQR)165

Descriptive statistics

Standard deviation92.313677
Coefficient of variation (CV)0.46836333
Kurtosis-1.0931842
Mean197.09843
Median Absolute Deviation (MAD)54
Skewness-0.44569605
Sum917099
Variance8521.8149
MonotonicityNot monotonic
2024-09-14T10:19:54.941303image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252 62
 
1.3%
255 59
 
1.2%
253 48
 
1.0%
251 46
 
0.9%
259 45
 
0.9%
260 44
 
0.9%
261 44
 
0.9%
249 43
 
0.9%
268 43
 
0.9%
250 42
 
0.9%
Other values (350) 4177
86.2%
(Missing) 192
 
4.0%
ValueCountFrequency (%)
0 3
 
0.1%
1 2
 
< 0.1%
2 8
0.2%
3 3
 
0.1%
4 4
0.1%
5 8
0.2%
6 5
0.1%
7 2
 
< 0.1%
8 7
0.1%
9 4
0.1%
ValueCountFrequency (%)
359 8
0.2%
358 4
0.1%
357 5
0.1%
356 6
0.1%
355 5
0.1%
354 6
0.1%
353 6
0.1%
352 9
0.2%
351 4
0.1%
350 9
0.2%

wspd
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9551022
Minimum0.7
Maximum36.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:55.429836image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile5.4
Q17.2
median9
Q311.5
95-th percentile18.4
Maximum36.7
Range36
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.2021055
Coefficient of variation (CV)0.42210571
Kurtosis4.5208078
Mean9.9551022
Median Absolute Deviation (MAD)2.2
Skewness1.7879991
Sum48232.47
Variance17.65769
MonotonicityNot monotonic
2024-09-14T10:19:55.808718image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 259
 
5.3%
8.3 255
 
5.3%
6.8 252
 
5.2%
7.2 243
 
5.0%
9 222
 
4.6%
8.6 221
 
4.6%
7.9 220
 
4.5%
6.5 214
 
4.4%
6.1 208
 
4.3%
9.7 200
 
4.1%
Other values (81) 2551
52.7%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
1.1 2
 
< 0.1%
1.4 1
 
< 0.1%
1.8 1
 
< 0.1%
2.5 1
 
< 0.1%
3.2 2
 
< 0.1%
3.6 3
 
0.1%
4 10
 
0.2%
4.3 37
0.8%
4.7 49
1.0%
ValueCountFrequency (%)
36.7 1
< 0.1%
35.3 1
< 0.1%
34.6 1
< 0.1%
32.4 1
< 0.1%
32 1
< 0.1%
31.3 2
< 0.1%
31 1
< 0.1%
30.6 2
< 0.1%
29.9 1
< 0.1%
29.5 2
< 0.1%

wpgt
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.937653
Minimum9.4
Maximum119.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:56.137456image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile17.3
Q124.1
median31.7
Q342.1
95-th percentile64.1
Maximum119.9
Range110.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.691284
Coefficient of variation (CV)0.42050003
Kurtosis1.6868428
Mean34.937653
Median Absolute Deviation (MAD)8.6
Skewness1.194261
Sum169272.93
Variance215.83383
MonotonicityNot monotonic
2024-09-14T10:19:56.330210image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.8 75
 
1.5%
21.6 73
 
1.5%
24.1 70
 
1.4%
24.5 68
 
1.4%
31.7 68
 
1.4%
26.6 67
 
1.4%
25.2 67
 
1.4%
23.4 65
 
1.3%
28.4 65
 
1.3%
20.9 64
 
1.3%
Other values (224) 4163
85.9%
ValueCountFrequency (%)
9.4 1
 
< 0.1%
10.1 1
 
< 0.1%
10.8 4
 
0.1%
11.5 3
 
0.1%
11.9 4
 
0.1%
12.2 5
0.1%
12.6 5
0.1%
13 6
0.1%
13.3 11
0.2%
13.7 11
0.2%
ValueCountFrequency (%)
119.9 1
< 0.1%
118.8 1
< 0.1%
100.4 1
< 0.1%
100.1 1
< 0.1%
98.6 1
< 0.1%
97.2 1
< 0.1%
96.1 1
< 0.1%
95.8 1
< 0.1%
95 1
< 0.1%
94 1
< 0.1%

pres
Real number (ℝ)

Distinct464
Distinct (%)9.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1016.8828
Minimum980
Maximum1044.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:56.521681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum980
5-th percentile1003
Q11012.1
median1016.8
Q31021.7
95-th percentile1030.9
Maximum1044.9
Range64.9
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation8.1396389
Coefficient of variation (CV)0.0080045004
Kurtosis0.62584098
Mean1016.8828
Median Absolute Deviation (MAD)4.8
Skewness-0.06126767
Sum4922729.7
Variance66.253721
MonotonicityNot monotonic
2024-09-14T10:19:56.708465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1017.7 45
 
0.9%
1016.8 39
 
0.8%
1016.7 38
 
0.8%
1017.5 37
 
0.8%
1013.8 37
 
0.8%
1013.9 35
 
0.7%
1020.1 34
 
0.7%
1015.8 34
 
0.7%
1018.1 34
 
0.7%
1017.1 34
 
0.7%
Other values (454) 4474
92.3%
ValueCountFrequency (%)
980 1
< 0.1%
984.3 1
< 0.1%
986 2
< 0.1%
987 1
< 0.1%
987.9 2
< 0.1%
988.1 1
< 0.1%
988.7 1
< 0.1%
989.8 1
< 0.1%
991.3 1
< 0.1%
991.4 1
< 0.1%
ValueCountFrequency (%)
1044.9 1
< 0.1%
1043.3 1
< 0.1%
1042.2 1
< 0.1%
1041.8 2
< 0.1%
1041.1 1
< 0.1%
1040.9 2
< 0.1%
1040.8 1
< 0.1%
1040.7 1
< 0.1%
1040.5 1
< 0.1%
1040.2 2
< 0.1%

tsun
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct492
Distinct (%)10.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean321.92876
Minimum0
Maximum943
Zeros766
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size75.7 KiB
2024-09-14T10:19:56.906112image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142
median276
Q3558
95-th percentile804
Maximum943
Range943
Interquartile range (IQR)516

Descriptive statistics

Standard deviation278.82553
Coefficient of variation (CV)0.86610941
Kurtosis-1.1328208
Mean321.92876
Median Absolute Deviation (MAD)246
Skewness0.41298618
Sum1559101
Variance77743.676
MonotonicityNot monotonic
2024-09-14T10:19:57.081020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 766
 
15.8%
6 99
 
2.0%
12 71
 
1.5%
18 63
 
1.3%
24 47
 
1.0%
30 46
 
0.9%
42 44
 
0.9%
60 39
 
0.8%
72 37
 
0.8%
36 37
 
0.8%
Other values (482) 3594
74.2%
ValueCountFrequency (%)
0 766
15.8%
1 9
 
0.2%
2 5
 
0.1%
3 5
 
0.1%
4 1
 
< 0.1%
6 99
 
2.0%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
12 71
 
1.5%
ValueCountFrequency (%)
943 1
 
< 0.1%
942 1
 
< 0.1%
937 1
 
< 0.1%
936 5
0.1%
930 2
 
< 0.1%
929 1
 
< 0.1%
924 5
0.1%
922 1
 
< 0.1%
921 1
 
< 0.1%
918 7
0.1%

year
Real number (ℝ)

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.5461
Minimum2011
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2024-09-14T10:19:57.229036image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2012
Q12014
median2018
Q32021
95-th percentile2024
Maximum2024
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8495911
Coefficient of variation (CV)0.0019080561
Kurtosis-1.1799506
Mean2017.5461
Median Absolute Deviation (MAD)3
Skewness-0.0017265595
Sum9775011
Variance14.819352
MonotonicityIncreasing
2024-09-14T10:19:57.430906image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2012 366
 
7.6%
2016 366
 
7.6%
2020 366
 
7.6%
2013 365
 
7.5%
2014 365
 
7.5%
2015 365
 
7.5%
2018 365
 
7.5%
2019 365
 
7.5%
2021 365
 
7.5%
2022 365
 
7.5%
Other values (4) 1192
24.6%
ValueCountFrequency (%)
2011 214
4.4%
2012 366
7.6%
2013 365
7.5%
2014 365
7.5%
2015 365
7.5%
2016 366
7.6%
2017 364
7.5%
2018 365
7.5%
2019 365
7.5%
2020 366
7.6%
ValueCountFrequency (%)
2024 249
5.1%
2023 365
7.5%
2022 365
7.5%
2021 365
7.5%
2020 366
7.6%
2019 365
7.5%
2018 365
7.5%
2017 364
7.5%
2016 366
7.6%
2015 365
7.5%

month
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5351909
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2024-09-14T10:19:57.569681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4174
Coefficient of variation (CV)0.52292275
Kurtosis-1.1780583
Mean6.5351909
Median Absolute Deviation (MAD)3
Skewness-0.018716838
Sum31663
Variance11.678623
MonotonicityNot monotonic
2024-09-14T10:19:57.684467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 434
9.0%
8 434
9.0%
6 420
8.7%
10 403
8.3%
12 403
8.3%
3 403
8.3%
5 403
8.3%
1 402
8.3%
9 395
8.2%
11 390
8.0%
Other values (2) 758
15.6%
ValueCountFrequency (%)
1 402
8.3%
2 368
7.6%
3 403
8.3%
4 390
8.0%
5 403
8.3%
6 420
8.7%
7 434
9.0%
8 434
9.0%
9 395
8.2%
10 403
8.3%
ValueCountFrequency (%)
12 403
8.3%
11 390
8.0%
10 403
8.3%
9 395
8.2%
8 434
9.0%
7 434
9.0%
6 420
8.7%
5 403
8.3%
4 390
8.0%
3 403
8.3%

Interactions

2024-09-14T10:19:48.289972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:25.741634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:27.959986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:29.793587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:32.192812image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:33.944111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:35.731279image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:38.329398image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:40.424111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:42.400754image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:44.316977image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:46.272648image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:48.457416image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:26.150743image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:28.119661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:29.925855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:32.360224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:34.074327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:35.876853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:38.471617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:40.560001image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:42.576702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:44.452835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:46.436785image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:48.653179image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:26.420907image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:28.288598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:30.095088image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:32.518477image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:34.221217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:36.033087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:38.632294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:40.710196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:42.771880image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:44.608565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:46.614669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:48.823335image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:26.595741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:28.434190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:30.295411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:32.659615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:38.789335image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:30.842950image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:33.090550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:34.815867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:29.171768image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:36.989169image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:39.825224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:41.689896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:43.708293image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:31.396090image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:33.530257image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:35.313658image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:37.148941image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:39.982867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:41.839297image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:43.884299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:33.673103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:19:33.817001image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:35.591430image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:38.181040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:40.268634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:42.225134image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:44.172176image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:46.067621image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:19:48.118867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2024-09-14T10:19:57.826835image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
monthprcppressnowtavgtmaxtmintsunwdirwpgtwspdyear
month1.000-0.0140.029-0.2260.1690.1400.209-0.030-0.035-0.162-0.197-0.101
prcp-0.0141.000-0.3480.033-0.012-0.0740.089-0.4430.2510.4250.312-0.007
pres0.029-0.3481.0000.071-0.214-0.191-0.2350.080-0.005-0.307-0.230-0.022
snow-0.2260.0330.0711.000-0.411-0.406-0.402-0.188-0.007-0.0400.042-0.034
tavg0.169-0.012-0.214-0.4111.0000.9800.9620.5510.0070.101-0.0670.039
tmax0.140-0.074-0.191-0.4060.9801.0000.9020.657-0.0440.069-0.1140.045
tmin0.2090.089-0.235-0.4020.9620.9021.0000.3760.0830.129-0.0160.023
tsun-0.030-0.4430.080-0.1880.5510.6570.3761.000-0.201-0.087-0.1940.084
wdir-0.0350.251-0.005-0.0070.007-0.0440.083-0.2011.0000.1980.179-0.012
wpgt-0.1620.425-0.307-0.0400.1010.0690.129-0.0870.1981.0000.8480.039
wspd-0.1970.312-0.2300.042-0.067-0.114-0.016-0.1940.1790.8481.000-0.013
year-0.101-0.007-0.022-0.0340.0390.0450.0230.084-0.0120.039-0.0131.000

Missing values

2024-09-14T10:19:50.424277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-14T10:19:50.745982image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-14T10:19:50.971163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datetavgtmintmaxprcpsnowwdirwspdwpgtprestsunyearmonth
02011-06-0111.59.713.34.20.0288.014.044.61021.70.020116
12011-06-0213.410.917.10.00.042.010.832.81026.96.020116
22011-06-0318.012.823.80.00.077.015.143.61022.9378.020116
32011-06-0420.614.627.80.00.080.012.231.31013.7570.020116
42011-06-0519.813.927.14.70.0269.09.767.71007.7480.020116
52011-06-0616.912.724.613.10.0244.08.655.11005.6264.020116
62011-06-0719.412.125.20.00.0121.09.034.91004.3624.020116
72011-06-0815.512.519.31.90.0261.012.651.51006.318.020116
82011-06-0913.712.315.40.40.0310.07.619.81014.90.020116
92011-06-1014.410.918.60.00.0329.06.821.21015.5150.020116
datetavgtmintmaxprcpsnowwdirwspdwpgtprestsunyearmonth
27942024-08-2718.312.924.60.00.087.08.6031.701019.9380.020248
27952024-08-2821.314.329.60.00.0NaN5.0034.091017.0691.020248
27962024-08-2922.915.631.20.00.0138.06.1023.801017.4773.020248
27972024-08-3022.515.230.80.00.093.06.1024.501016.9763.020248
27982024-08-3123.115.730.00.00.064.08.3033.801017.1686.020248
27992024-09-0123.217.429.50.00.065.07.2022.001013.9564.020249
28002024-09-0222.018.427.20.00.0328.07.6030.201012.8493.020249
28012024-09-0322.816.828.90.00.0294.05.4018.701013.9658.020249
28022024-09-04NaNNaNNaNNaN0.0NaN8.4329.83NaNNaN20249
28032024-09-05NaNNaNNaNNaN0.0NaN8.4329.83NaNNaN20249